The present invention relates generally to dipole moment detection and localization, and more particularly, to split array dipole moment detection and localization apparatus and methods for detecting surface and subsurface vessels.
Reference is made to U.S. Patent Application Ser. No. 07/616,158, filed Nov. 20, 1990, for "Dipole Moment Detection and Localization," assigned to the assignee of the present invention, the contents of which is incorporated herein by reference. The dipole moment detection and localization process described in this patent application has been demonstrated to yield dramatic performance improvement over currently available magnetic anomaly detection systems using a single sensor to detect a change in the local magnetic field. This dipole moment detection and localization process uses an array or multiple arrays of magnetic sensors and digital signal processing techniques to process the magnetic field's x, y, and z components for vector sensors and the total field component for scalar sensors at each of a plurality of positions relative to the array of sensors. In doing this, a magnetic signature of the magnetic field of a magnetic dipole located in the field is created. This magnetic signature provides an easily recognizable feature for an automatic pattern recognizing system. This process precomputes predicted target magnetic signatures for multiple orientations of the dipole at each of a plurality of range locations, and store them in a lookup table for magnetic signature matching.
Input data comprising the magnetic field strengths measured by the sensors are processed against a predicted background ambient noise using a linear model, where each sensor's output value is predicted using other sensors of the array, and a long term time average consistent with the relative motion of a target. This amounts to bandpass filtering or long term averaging of the signals from the sensor array. The bandpass filtered data is used to update the predicted data so that anomalies and other non-target data are removed from the signals that are processed. The sensor data is then processed against a set of Anderson adjoint matrices, which are a set of mathematical functions (Anderson functions) that decompose the magnetic field into its components in each of the maximum response locations for each dipole orientation.
The resulting data is expressed in terms of sensed Anderson coefficients and this data is matched filtered, wherein it is mathematically correlated by means of a dot product with a set of stored precomputed predicted target signatures (precomputed Anderson coefficients). The dot product, or correlation, of these two set of data yields a set of values including the largest value indicating a magnetic signature matches in the set of correlated data. The data is then normalized. This normalized data is then thresholded, and if a target is present at any one of the maximum response locations, then the correlated, dot product, normalized value computed as stated above will be higher than the chosen threshold.
Notwithstanding the benefits provided by the above-cited invention, it has been found that improved performance can be had by modifying its processing steps.